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Abstract:

Systems and methods for assessing a patient's neurologic state based on
auditory evoked responses are provided.

Claims:

1-23. (canceled)

24. A system for processing event-related brain potential signals of a
patient, comprising: one or more neurological electrodes configured to
collect event-related brain potential signals generated in response to a
stimulus provided to the patient; one or more stimulus generators
configured to provide sensory or cognitive stimuli, or motor events to
the patient; a signal processor operatively connected to the one or more
neurological electrodes and configured to receive the event-related brain
potential signals; and wherein the signal processor s configured to:
denoise the event-related brain potential signals received by the signal
processor; transform the denoised event-related brain potential signals
to a transform domain; and automatically detect locations of one or more
peaks of the event-related brain potential signals in the transform
domain.

25. The system of claim 24, wherein the processor configured to reject
artifacts present in the event-related brain potentials prior to
denoising.

26. The system of claim 25, wherein the processor is configured to reject
artifacts present in the event-related potentials using a plurality of
automated artifact detectors working in parallel.

27. The system of claim 24, wherein the processor is configured to
transform the denoised event-related brain potential signals to a wavelet
transform domain.

28. The system of claim 24, wherein the processor is configured to
extract a set of quantitative features from the event-related brain
potential signals.

29. The system of claim 28, wherein the processor is configured to
compare the set of quantitative features to one or more feature sets
stored in a storage system to allow assessment or monitoring of the
patient's neurological state.

30. The system of claim 24, wherein the processor is configured to
extract features related to the peaks in the patient's event-related
potential signals.

31. The system of claim 30, wherein the processor is configured to detect
the amplitude or the timing of the peaks in the patient's event-related
potential signals.

32. The system of claim 24, wherein the processor is configured to locate
peaks of the event-related potential signals in time domain.

33. The system of claim 32, wherein the processor is configured to
combined the peak locations in time domain with the peak locations in the
transform domain.

34. The system of claim 24, wherein the stimulus provided to the patient
is an auditory stimulus and the event-related potential signal generated
in an auditory brainstem response.

35. The system of claim 24, wherein the stimulus provided to the patient
is a visual stimulus.

Description:

[0001] The present disclosure pertains to devices and methods for
monitoring changes in a neurologic state of a patient, and more
particularly, to systems and methods for monitoring auditory evoked
response.

[0002] There are numerous surgical and medical conditions that can cause
potentially deleterious changes in brain or brain stem function. For
example, moderate or severe central nervous system injury can results
from trauma (e.g., due to an impact or other injury to the head),
metabolic disorders, infections, expanding intracranial masses,
intracranial hemorrhage, illicit or prescription drug use, and iatrogenic
sources (e.g., post-operatively or as a medical treatment side effect).
Whatever the cause, it would be desirable to have better noninvasive
methods for evaluating head injury and, when needed, providing
appropriate medical or surgical interventions before potentially serious
or irreversible neurological damage occurs. In addition, portable
neurologic monitors that allow assessment of head injuries at more remote
locations (e.g., on the battlefield or at accident sites) may allow more
appropriate patient assessment and treatment.

[0003] In the clinical setting, changes in neurologic state may be
suspected based on declining mental status, abnormal neurological signs,
and other physical findings, such as changes in the appearance of the
optic nerve when viewed through an ophthalmoscope. However, monitoring
neurologic status through these methods presents a number of challenges.
For example, many surgical patients or seriously ill medical patients
will be sedated or unconscious, thereby making it impossible to evaluate
certain changes in mental status. In addition, changes in physical exam
findings, such as a change in the optic nerve, may be discovered after
significant neurologic damage has occurred, thereby preventing timely
intervention. In addition, plantable monitors are less desirable since
they require an invasive procedure and impart other potential risks
(e.g., infection).

[0004] The systems and methods of the present disclosure to provide
easy-to-use tools for assessing and monitoring head injuries.

SUMMARY

[0005] A system for monitoring brain electrical activity is provided. The
system comprises a set of electrodes, at least one auditory stimulus
generator, and a detection system operatively connected to the set of
electrodes and configured to receive electrical signals detected by the
electrodes after production of an auditory stimulus by the stimulus
generator, the electrical signals representing an auditory evoked
response. The system further comprises a processor circuit including
electrical circuitry configured to perform the steps of: removing
artifact noise from the signal; performing a nonlinear denoising step on
the signal; performing a non-linear transform on the signal; producing a
set of nonlinear features related to the patient's auditory brain stem
response; and comparing the set of nonlinear features to one or more
feature sets stored in a storage system and determining if the non-linear
features are indicative of an abnormal neurologic state.

[0006] A method for monitoring brain electrical activity is provided. The
method comprises applying a set of electrodes to a patient, generating at
least one auditory stimulus that can be detected by the patient, and
recording an electrical signal detected by the electrodes after
production of an auditory stimulus by the stimulus generator, the
electrical signal representing an auditory evoked response; removing
artifact noise from the signal. The method further comprises performing a
non-linear denoising step on the signal; performing a non-linear
transform on the signal; producing a set of non-linear features of the
signal; and comparing the set of non-linear features to one or more
feature sets stored in a storage system and determining if the non-linear
features are indicative of an abnormal neurologic state.

DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1A illustrates a brain electrical activity monitoring system
according to one embodiment of the present disclosure.

[0009]FIG. 2A illustrates an electrode set for use with the brain
electrical activity monitoring system of the present disclosure.

[0010] FIG. 2B illustrates the electrode set of FIG. 1B, as applied to a
patient.

[0011]FIG. 3 illustrates a method for automatically processing a signal
to assess a neurologic state of a patient, according to certain
embodiments.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

[0012] The present disclosure provides a system and method for monitoring
brain electrical activity, including assessment of auditory brainstem
responses (ABR) to assess neurologic function. In certain embodiments,
the system and method allow rapid, automatic, and/or continuous
monitoring of ABR signals, or other evoked potential signals. The system
and method can assist in determining the severity of certain injuries
due, for example, to trauma, infection, medical disorders (e.g.,
inflammatory disorders, adverse drug reactions), and/or post-surgical
complications. In certain embodiments, the systems and methods allow
rapid assessment of the severity and/or progression of problems due to
traumatic brain injury due, for example to an impact to the head. In
certain embodiments, the systems and methods allow continuous,
non-invasive monitoring of intracranial pressure (ICP).

[0013] As described further below, the system includes a set of electrodes
and an auditory stimulus for generating and detecting ABR and other
auditory evoked potential signals from a patient. The system and method
further include processes for automatically removing raw artifact noise
from the signal and performing a non-linear denoising step on the signal
to generate a sufficiently high signal-to-noise ratio to allow automatic
ABR evaluation. The system and method can further include non-linear
processing techniques including, for example, performing a non-linear
transform on the signal and producing a set of non-linear features
related to the patient's auditory brain stem response. These non-linear
features can be compared to one or more feature sets stored in a storage
system to determine if the non-linear features are indicative of an
abnormal neurologic state.

[0014] FIG. 1A illustrates a brain electrical activity monitoring system
10, according to certain embodiments of the present disclosure. As shown,
the brain electrical activity monitoring system 10 can include an
enclosure 20 containing electrical circuitry configured to perform data
processing, stimulus generation, and analysis for diagnosis and patient
monitoring. In addition, the enclosure 20 may further include a display
system 30, such as an LCD or other visual display to provide real-time,
easy-to-interpret information related to a patient's clinical status.

[0015] In some embodiments, the brain electrical activity monitoring
system 10 will include circuitry configured to provide real-time
monitoring of brain electrical activity. The system 10 will provide rapid
data acquisition, processing, and analysis to allow point-of-care
diagnosis and assessment. For example, as shown, the display system 30
can include one or more indicators 35, or visual displays, that are
configured to display an easy-to-interpret indication of a patient's
status. In one embodiment, the indicators 35 will include an indication
of where a patient's status lies relative to a normal data set, a
patient's status relative to a base line, and/or one or more indicators
of the origin of any abnormalities. In some embodiments, the indicators
provide a scale (from normal to severely abnormal). Accordingly, the
scale provides an indication of the severity of an injury, elevation in
ICP, or abnormality in brain stem function,

[0016] In addition, the brain electrical activity monitoring system 10 may
include one or more alert systems for notifying a caregiver of an
abnormality or changing condition. In some embodiments, the system
further includes a communication device configured to automatically
generate a signal representing the patient's neurologic state. Such
communication devices can include visual display systems and/or audible
alerts that may be easily understood by patient care givers. In addition,
in some embodiments, the alert systems can be remote from the monitoring
system, to allow remote monitoring and intervention by health care
personnel.

[0017] In certain embodiments, the visual display indicates a deviation
from a baseline measurement, as described further below. In addition, the
system can include at least one second visual display indicating at least
one diagnostic state. For example, the diagnostic state can indicate,
elevated intracranial pressure, cerebral edema, compromised brainstem
function, or dysfunction of higher parts of the neural auditory pathway,
including the cognitive function relating to auditory stimulus
perception. In some embodiments, the communication system includes a
visual display indicating a deviation from a baseline measurement
indicative of ICP for a patient.

[0018] FIG. 1B illustrates a schematic diagram of the monitoring system of
FIG. 1A, illustrating additional components. As shown, the enclosure 20,
can include a number of component parts. For example, the enclosure 20
may include a memory unit or storage system 22 configured to store data
related to patient brain electrical activity data measurements, or a
database of normal and/or pathological readings. Further, the enclosure
will include circuitry configured to process and evaluate electrical
signals and data 24, and a transmitter unit 26.

[0019] The circuitry 24 can include a number of circuitry types. For
example the circuitry 24 can include processing circuitry configured to
receive electrical signals from electrodes, as shown in FIGS. 2A-2B, and
to convert such signals into data that can be further evaluated. In some
embodiments, the circuitry can be configured to enable nonlinear
processing, including nonlinear amplifiers. Further, the circuitry 24 can
also include components configured to allow analysis of processed data
and comparison of brain electrical activity data to normal data, or to
previous or future measurements, as described in more detail below.
Further, it will be understood that, although shown as a single
component, multiple components can be included, either on a single chip
or multiple chips.

[0020] The transmitter unit 26 can include a number of transmitter types.
For example, the transmitter 26 may include a hardware connection for a
cable or a telemetry system configured to transmit data to a more distant
receiver 28, or a more powerful transmission system to redirect data to a
database 32 that may be stored nearby or at a remote or distant location.
In certain embodiments, the data can be transmitted and stored and/or
evaluated at a location other than where it is collected.

[0021] The brain electrical monitoring system 10 may be configured to
attach to various patient interfaces. For example, FIGS. 2A-2B illustrate
an electrode set 50 for use with the brain electrical activity monitoring
system 10 of the present disclosure. As shown, the electrode set 50
includes one or more electrodes 60 for placement along the patient's
forehead and mastoid region. As shown, the electrode set 50 includes a
limited number of electrodes 60 to facilitate rapid and easily repeated
placement of the electrodes 60 for efficient, but accurate, patient
monitoring. Further, in one embodiment, the electrodes 60 may be
positioned on a head band 70 that is configured for easy and/or rapid
placement on a patient, as shown in FIG. 2B. Further, it will be
understood that other electrode configurations may be selected, which may
include fewer or more electrodes.

[0022] As noted, the electrode set 50 will be operably connected to the
monitoring system 10. Generally, the electrodes 60 will be electrically
coupled with the monitoring system 10 to allow signals received from the
electrodes to be transmitted to the monitoring system 10. Such an
electrical coupling will generally be through one or more electrical
wires, but nonphysical connections may also be used.

[0023] Further, as shown, a signal production device 80 may be provide,
and may be attached to the head band 70 or contained separately. As
shown, the device 80 includes an auditory stimulus generator configured
to produce audible signals to facilitate measurement of brain electrical
activity in response to auditory stimuli. Further, the monitoring system
10 may also include other stimulation generating systems such as visual,
tactile, taste, and olfactory stimulation systems. Further, the
stimulation devices may be attached to the electrode set 50 or may be
contained in separate components.

[0024] In some embodiments, the electrode set 50 will include electrodes
positioned to allow detection of various types of brain electrical
activity. For example, various forehead or scalp electrodes may be
included to allow detection of cortical activity, or to assist in
identification of signal artifacts to be removed during raw denoising.
Further, other electrodes may be positioned to allow detection of brain
stem functions (e.g., mastoid or occipital electrodes). In some
embodiments, the electrode set is positioned on a head band and includes
at least two electrodes positioned on the head band to allow detection of
auditory evoked response signals when the headband is positioned on a
patient.

[0025]FIG. 3 illustrates a method for automatically processing a signal
to assess a neurologic state of a patient, according to certain
embodiments. As shown, the process includes application of an electrode
set to a patient's head, as shown at Step 310. Next, the brain activity
monitoring system is connected to the electrode set, as shown at Step
320, and data collection is begun, as shown at Step 330. As noted above,
the data collection can include measurement and recording of ABR signals
after generation of audible stimuli produced by a stimulus generator.

[0026] After collection of ABR or other evoked auditory response signal
data, the data can be processed to allow automatic neurologic assessment
and monitoring. Accordingly, raw denoising is first performed, as shown
at Step 340 to reduce signal artifacts. The raw denoising can be
performed using an automated process that does not require a trained
technician, as described in more detail below.

[0027] After raw denoising, a rapid non-linear denoising process is used
to produce a suitable signal-to-noise ratio. In certain embodiments, a
wavelet denoising algorithm is used. For example, a suitable denoising
algorithm include Cyclic Shift Tree Denoising (CTSD), which is described
by Causevic et al. in "Fast Wavelet Estimation of Weak Biosignals,"
Biomedical Engineering, Vol. 52(6): 1021-32, 2005. In certain
embodiments, to facilitate automatic, real-time monitoring, a CTSD
process may be performed real time, such that incoming data is buffered
and the algorithm is completed on the buffered date. In addition, as new
data is received (i.e., a new data frame comes in), the new data can be
is added to the buffer on a first in/first out basis, and the algorithm
is repeated.

[0028] In some embodiments, the CTSD method is adapted for continuous
measurement, such that new frames are adapted into the algorithm in real
time in batches. For example, a time at which the CTSD is performed can
be set, and as each level of CTSD progresses, a new epoch of fresh ABR
data is inserted into the process in parallel. In certain embodiments, a
linear averaging process can be employed to arrive at an averaged
waveform, synchronized to the auditory stimulus. This result can be
combined with the CTSD result, sample by sample, or averaged.

[0029] After denoising, the signal can be further processed to identify
certain non-linear features, as shown at step 360. In certain
embodiments, a non-linear transform is performed followed by a process
for detecting the location of ABR peaks in the non-linear domain. In
addition, various other non-linear features can be identified and stored
for comparison to patient baseline, normative, or population data, as
described further below.

[0030] In various embodiments, automatic peak detection can be performed
by using a set of non-linear methodologies, such as a non-linear
transform (e.g., a wavelet transform), while keeping the CTSD
coefficients in the non-linear/wavelet domain and searching for local
peaks in that domain independently. The peaks information in the
non-linear domain is then combined with the time domain peak detection
methods in a single classifier, or a using a voting classifier scheme.

[0031] In addition, various other non-linear features can be extracted
from the signal, such that in addition to the actual peak locations,
other qualitative information about the peaks is calculated, including,
for example, various local and global maxima of the non-linear features
(including number and location, nth order moments, vanishing moments,
area under the curve of non-linear coefficients, etc. In certain
embodiments, linear features of the waveform can be extracted such as
amplitude, power, phase, frequency spectrum, or others, and those
features can be combined with non-linear features.

[0032] After peak detection and feature extraction are performed, the
non-linear and/or linear features can be compared to data stored in a
database and/or to prior data obtained from the same patient to allow
assessment and monitoring of the patient's neurologic state, as shown at
Step 380. In addition, if abnormalities are detected, an alarm or
indicator can be active, as shown at step 390, or a normal condition can
be indicated. Further, if no abnormality is detected, or if continued
monitoring of an abnormal patient is needed, measurements can be repeated
continuously or periodically, to allow ongoing patient monitoring. In
some embodiments, this comparison may include a multivariate comparison.

[0033] In some embodiments, the database includes prior auditory evoked
response measurements for the same patient, and the set of non-linear
features are compared to one or more feature sets stored from the prior
measurements and determine if any changes have occurred.

[0034] In various embodiments the database includes ABR or other evoked
potential data from a group of other patients having an identified
neurologic state. For example, the database can include a database of
normal patients and patients with a variety of different abnormalities,
including, for example, traumatic brain injury at various times after
injury, infection, edema, elevated ICP. In some embodiments, the system
includes a database of auditory evoked response data for a group of
patients, and the patient's neurologic state is classified based on a
similarity between one or more non-linear features of the patient's
auditory evoked response and one or more non-linear features of at least
one other patient having a known neurologic state.

[0035] A variety of non-linear features can be used to assess the
patient's neurologic state. For example, the timing of ABR peaks has been
shown to change due to trauma and/or increased intracranial pressure.
However, automatic detection of ABR peaks is difficult, and therefore,
automatic assessment of brain abnormalities has not been successful. The
signal processing techniques of the present disclosure allow automatic
peak detection and feature extraction in the non-linear domain, and
therefore, facilitate automatic neurologic monitoring. Accordingly, in
certain embodiments, the non-linear features set identified as described
above, can include the location, amplitude or time of one or more peaks
in an auditory evoked response.

[0036] As noted above, in various embodiments, the system and method of
the present disclosure can provide an indication of an elevation in ICP.
In some embodiments, the indication can be based on a sliding scale from
normal to severely abnormal, without providing an absolute value of ICP.
In this way, the system provides information of clinical significance,
for example, warning of potential deleterious changes in brain stem
function, as indicated by changes in ABR, without the need for an
invasive ICP monitor. In other embodiments, a correlation between ICP and
the ABR data is made to provide an estimation of ICP.

EXAMPLE

Sample Algorithm

[0037] One typical specific algorithm for feature extraction and
classification is described below. This algorithm may be used to identify
abnormalities in ICP or assess the severity of a traumatic brain injury:

[0044] Put together all the features in a vector, multiplying each of the
features with a pre-determined weight factor based on a training data set
with manually pre-identified peaks and invasive ICP recordings, and then
classify new signals.

Raw Denoising:

[0045] Most systems that rely on quantitative analysis of brain electrical
activity typically assume that a trained technologist has manually edited
the raw data to remove artifacts. However, the editing process can be
time-consuming and is inherently subjective. In addition, the need for
technologist editing prevents automated monitoring or assessment, and
therefore, is not suitable for continuous and rapid monitoring, or for
use in many settings (e.g., in a field hospital, at a sporting event, or
in typical primary care settings). The following processing techniques
can be used for raw data denoising to allow automatic denoising. Further,
suitable methods for editing or denoising EEG or other signals are
described in U.S. patent application Ser. No. 12/720,861, which is
titled, "Method and Device for Removing EEG Arifacts," was filed on Mar.
10, 2010, and is incorporated by reference in its entirety. This is
accomplished using standard signal processing components, which include
digital filtering (low-pass filtering, bandpas filtering, etc.),
thresholding, peak detection, and frequency-based processing.

[0046] There are seven typical types of noise that can contribute to poor
signal quality. These include (1) horizontal lateral eye movements (HEM),
(2) vertical eye movements (e.g. blinks) (VEM), (3) cable or electrode
movement causing over-range artifacts (PCM), (4) impulse artifacts (for
example due to electrode "pops") (IMP), (5) electromyographic activity
(also referred to as "muscle activity") (EMG), (6) significantly low
amplitude signal (for example as a result of the suppression component of
"burst suppression") (SLAS), and (7) atypical electrical activity pattern
(for example due to paroxysmal brain activity) (REAP). Out of these seven
artifact types, two are non-physiological (type 3, type 4), three are
physiological but are not brain-generated (type 1, 2, type 5) and two are
brain-generated (type 6, type 7). All of these artifacts reflect a
non-brain electrical activity, or abnormal brain-electrical activity.
Further, in addition to recognizing artifacts of the types listed above,
technologists typically remove short segments of the signal located (in
time) immediately before and after the artifact. These segments are
traditionally referred to as guardbands.

[0047] The automated denoising process described below includes artifact
detector algorithms that can be used to independently identify the
artifacts described above. These artifact detectors can work in parallel
on a raw ABR data stream. In some embodiments, the duration of each
artifact segment is computed to a resolution of 150 ms, each 15 ms
segment referred to as a "sub-epoch". Each artifacting module produces a
binary mask of size 1×10 indicating presence or absence of the
artifact type in each of the sub-epochs.

[0048] The seven types of artifacts and the algorithms used for their
detection are described below.

[0049] (1) Horizontal/Lateral Eye Movement (HEM/LEM):

[0050] To remove HEM artifacts, each electrode channel is band-pass
filtered using an FIR filter with passband 0.5-3 Hz. The high-pass
cut-off frequency of 0.5 Hz is chosen to ignore the influence of
low-frequency activity occurring at frequencies below the delta--1
band (0.5-1.5 Hz). Candidate HEM sub-epochs are identified wherever the
difference signal F7f F8f exceeds a threshold of 24 μV. An additional
measure, the mean-squared-error (mse) between -F7f and F8f is computed to
help filter-out false detection of HEM. Cases where the mse is large
(above a threshold) are indicative of an asymmetry between the two leads,
which is likely to reflect presence of pathology rather than presence of
HEM.

[0051] (2) Vertical eye movement(VEM)/Eye Opening/Eye Closing (EOEC):

[0052] Detection of vertical eye movement (VEM) (of which eye
opening/closing is a sub-type) is performed by locating large "bumps" on
leads Fp1 and Fp2, which are located right above the eyes. Since both
eyes generally move in unison, the algorithm makes sure that only bumps
that occur concurrently and in the same direction (same polarity) on Fp1
and Fp2 are identified as vertical eye movements. Each of the two
signals, Fp1 and Fp2, is first low-pass filtered in the range 0.5-5 Hz.

[0053] Sub-epochs are then analyzed one at a time. In each sub-epoch, runs
of samples exceeding a threshold of 24 μV are identified. In each such
run, the global extremum is located and its value is compared to average
signal values on either side of it. If te denotes the time location
of the extremum (in milliseconds), these average are taken over temporal
windows [te-320, te-100] and [te+100, te+320]. If the
absolute difference between the extremum and either average exceeds the
threshold, the sub-epoch is identified as a candidate VENT artifact.
After this processing has occurred on both leads, the results are
combined to turn candidate VEMs to true VEMs wherever they occurred
concurrently on Fp1, Fp2 as described above.

[0054] (3) Patient cable or electrode movement (PCM):

[0055] This is simply done by detecting excessively large signal
magnitudes (also called "over-range") in any recorded channel. The
default magnitude threshold is set to 120 μV. Generally, no guardband
is implemented for artifacts of this type.

[0056] (4) Impulses (IMP):

[0057] To remove impulse artifacts, any recorded channel is first
high-pass filtered with cutoff frequency at 15 Hz. This is done in order
to remove the alpha component of cerebral electrical activity so that
"sharp alpha" waves are not labeled as spikes. In each sub-epoch, the
algorithm then looks at high-frequency activity. Successive windows of
100 ms width with 50% overlap are examined. Within each window, the value
(max-min) is computed and compared to a threshold equal to 75 μV. Data
greater than the 75 μV threshold is removed.

[0058] (5) Muscle activity (EMG):

[0059] To remove EMG artifacts, any recorded channel is first band-pass
filtered within the range 25-35 Hz (subband: β2) to produce a first
signal (E2) and band-pass filtered in the range 15-25 Hz (subband:
β1) to produce a second signal (E1). The variance (energy) of signal
E1 on each lead, over the entire 2.5 second long epoch is computed. For
each sub-epoch, the variance of signal E2 is also computed on each lead,
and the relative energy of this signal with respect to the energy of
signal E1 over the entire epoch is compared to a fixed threshold. The
default threshold value is 155%. If, in at least one lead, the relative
energy is larger than the threshold and the energy of E2 is larger than a
minimum energy (currently set to 14 μV2), EMG detection is triggered.

[0060] (6) Significantly Low Amplitude Signal (SLAS):

[0061] This is meant to capture extremely low-amplitude EEG signals (at
all frequencies), which occur, for example, when the brain is in burst
suppression mode; a condition which can occur (but should be avoided)
during anesthesia. No additional filtering of the EEG is used for
detection of this activity. It can be detected by looking for signal
epochs with mean-square energy below a threshold. Sub-epochs are examined
four at a time, corresponding to a window size of 1 second. The overlap
between consecutive groups of sub-epochs is 75%. The maximum signal
energy (across leads) is computed and compared to a fixed threshold. The
default threshold value is 12 μV2.

[0062] (7) Atypical Electrical Activity Pattern (AEAP):

[0063] This artifact type is meant to detect unusual patterns of activity
in the EEG such as those which occur in the EEG of epileptic subjects
during a convulsive or non-convulsive seizure. The algorithm is
sensitivite to Spike-Wave complexes occurring in bursts over several
hundred milliseconds. In this method raw EEG data is cleaned, and linear
and non-linear averaging of that pre-cleaned data is performed. Then the
linear and non-linear features of the single final averaged waveform is
used to detect peak location and amplitude (using direct methods of peak
detection and classification based on features, and to compare the
features of the present averaged waveform to the features of pre-stored
waveforms already correlated to ICP levels or other abnormalities.

[0064] Other embodiments will be apparent to those skilled in the art from
consideration of the specification and practice of the devices and
methods disclosed herein. It is intended that the specification and
examples be considered as exemplary only, with a true scope being
indicated by the following claims. A number of patents, patent
publication, and nonpatent literature documents have been cited herein.
Each of these documents is herein incorporated by reference.